Fuzzy Logic and the Semantic Web 1st Edition by Elie Sanchez ISBN 9780444519481

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ISBN 13: 9780444519481
Author: Elie Sanchez

These are exciting times in the fields of Fuzzy Logic and the Semantic Web, and this book will add to the excitement, as it is the first volume to focus on the growing connections between these two fields. This book is expected to be a valuable aid to anyone considering the application of Fuzzy Logic to the Semantic Web, because it contains a number of detailed accounts of these combined fields, written by leading authors in several countries. The Fuzzy Logic field has been maturing for forty years. These years have witnessed a tremendous growth in the number and variety of applications, with a real-world impact across a wide variety of domains with humanlike behavior and reasoning. And we believe that in the coming years, the Semantic Web will be major field of applications of Fuzzy Logic.
This book, the first in the new series Capturing Intelligence, shows the positive role Fuzzy Logic, and more generally Soft Computing, can play in the development of the Semantic Web, filling a gap and facing a new challenge. It covers concepts, tools, techniques and applications exhibiting the usefulness, and the necessity, for using Fuzzy Logic in the Semantic Web. It finally opens the road to new systems with a high Web IQ.
Most of today’s Web content is suitable for human consumption. The Semantic Web is presented as an extension of the current web in which information is given well-defined meaning, better enabling computers and people to work in cooperation. For example, within the Semantic Web, computers will understand the meaning of semantic data on a web page by following links to specified ontologies. But while the Semantic Web vision and research attracts attention, as long as it will be used two-valued-based logical methods no progress will be expected in handling ill-structured, uncertain or imprecise information encountered in real world knowledge. Fuzzy Logic and associated concepts and techniques (more generally, Soft Computing), has certainly a positive role to play in the development of the Semantic Web. Fuzzy Logic will not supposed to be the basis for the Semantic Web but its related concepts and techniques will certainly reinforce the systems classically developed within W3C.
In fact, Fuzzy Logic cannot be ignored in order to bridge the gap between human-understandable soft logic and machine-readable hard logic. None of the usual logical requirements can be guaranteed: there is no centrally defined format for data, no guarantee of truth for assertions made, no guarantee of consistency. To support these arguments, this book shows how components of the Semantic Web (like XML, RDF, Description Logics, Conceptual Graphs, Ontologies) can be covered, with in each case a Fuzzy Logic focus.

Fuzzy Logic and the Semantic Web 1st Table of contents:

On the Expressiveness of the Languages for the Semantic Web – Making a Case for `A Little More’
Abstract
Keywords
Introduction
Science
Information retrieval
Semantic Web
Some motivating examples
Glycomics
Information retrieval
Challenges for multi-valued logics
The fuzzy case
Probabilities and possibilities
Belief and trust
A framework for logical extensions on the Semantic Web
A fuzzy uncertain logic
OWL-FP: combining OWL and a fuzzy-probabilistic logic
Soundness and completeness
Fundamental limitations of the current standard
Related work
Conclusions
Acknowledgements
References
Fuzzy Ontologies for Information Retrievalon the WWW
Abstract
Keywords
Introduction
Information retrieval and the semantic web
Information retrieval issues
Relevance
Query formulation
Ontologies to support information retrieval
Uncertainty in information retrieval
Fuzzy search
Fuzzy ontology
Rationale
Description of fuzzy ontology
Methods of learning fuzzy ontologies
Learning from documents
Learning from users
Example of use of fuzzy ontology
Fuzzy AND operator
Fuzzy OR operator
Future research directions
Structural fuzzy ontology
Discussion
References
Capturing Basic Semantics ExploitingRDF-oriented Classification
Abstract
Keywords
Introduction
A look at the Semantic Web panorama
Framework
Logical view
Web semantic technologies
System behavior
Matrix building
Relevance criteria for eliciting the metadata/features
Instance & property relevance values
Candidate metadata
Clustering
Generation of fuzzy rules
Semantic interpretation through linguistic modifiers
A sketch about practical use
Experimental results
Conclusions
References
Fuzzy Description Logics for Ontology Construction
A Fuzzy Description Logic for the Semantic Web
Abstract
Keywords
Introduction
Preliminaries
Syntax
Concrete domains.
Alphabets.
RBox.
Concepts.
TBox.
ABox.
Knowledge base.
Semantics
Interpretation.
Satisfiability.
Logical consequence.
Fuzzy OWL DL
Preliminaries on fuzzy set theory
Fuzzy SHOIN(D)
Syntax
Concrete fuzzy domain.
Modifiers.
Fuzzy RBox.
Fuzzy TBox.
Fuzzy ABox.
Fuzzy knowledge base.
Semantics
Fuzzy interpretation.
Satisfiability.
Logical consequence.
Best truth value bound.
Reasoning
Related work
Conclusions and outlook
References
What Does Mathematical Fuzzy Logic Offer to Description Logic?
Abstract
From classical logic to fuzzy logic
to*
From description logic to fuzzy description logic
Examples and comments
to*
References
Possibilistic Uncertainty and Fuzzy Features in Description Logic. A Preliminary Discussion
Abstract
Introduction
Classes and instances – the fuzzy set setting
Basic features of a description logic
Concepts and relations interpreted as fuzzy sets
A fuzzy DL for dealing with typicality
Fuzzy sets as families of classical nested sets
Elements for a twofold description logic of vagueness
Possibilistic uncertainty and first-order possibilistic logic
Degrees of truth and degrees of uncertainty
A possibilistic logic refresher 6,17
Towards a possibilistic description logic
Hints for the handling of uncertainty and fuzzy features in description logic
Future work and concluding remarks
References
Uncertainty and Description Logic Programs over Lattices
Abstract
Keywords
Introduction
Preliminaries
A quick look to ALC
A quick look to description logic programs
Syntax.
Semantics.
The logic L-ALC
Logic-based multimedia information retrieval.
Decision algorithms in L-ALC
Uncertainty in description logic programs
Syntax.
Semantics.
Related work
Conclusion
Acknowledgements
References
Fuzzy Quantification in Fuzzy Description Logics
Abstract
Keywords
Introduction
Fuzzy description logics
Fuzzy assertions and truth-value bounds
Fuzzy terminological axioms
Cardinality and fuzzy quantification
Absolute cardinality
Relative cardinality
Fuzzy quantifiers
Evaluation of quantified sentences
Extending FDLs with fuzzy quantification
The ALCQ+F(D) language
Fuzzy knowledge base
Fuzzy interpretation
An example
Fuzzy satisfiability
Concept independence
Calculating fuzzy satisfiability
Atomic concepts
Negation
Absolute quantifiers
Relative quantifiers
Number of individuals
Calculating S(D)
Some particular cases
Disjunction
Calculating the lower satisfiability bound in the absence of independence
Satisfiability of the union of dependent and non-quantified concepts
Satisfiability of the union of dependent and quantified concepts
Conjunction
Summary
Conclusions
References
Search and Protoforms
From Search Engines to Question Answering Systems – The Problems of World Knowledge, Relevance, Dedu
Abstract
Introduction
The principal problems
The problem of world knowledge
The problem of relevance
The problem of precisiation of meaning – a prerequisite to mechanization of natural language underst
Test queries (Google)
The new tools
The concept of a generalized constraint
Principal modalities of generalized constraints
(a) Possibilistic (r=blank)
(b) Probabilistic (r=p)
(c) Veristic (r=v)
(d) Usuality (r=u)
(e) Random-set (r=rs)
(f) Fuzzy-graph (r=fg)
(g) Bimodal (r=bm)
(h) Group (r=g)
Operations on generalized constraints
(a) Conjunction
(b) Projection (possibilistic) (Figure 7)
(c) Projection (probabilistic)
(d) Propagation
Primary constraints, composite constraints and standard constraints
The generalized constraint language and standard constraint language
The concept of cointensive precisiation
Precisiation/imprecisiation principle (P/I principle)
Precisiation of propositions
(a) The Robert example
(b) The balls-in-box problem
(c) The tall Swedes problem
(d) The partial existence problem
The concept of a protoform
Protoformal deduction
(a) Computational rule of inference [37](b) Intersection/product syllogism [32, 33](c) Basic extension principle [24](d) Extension principle [38](e) Basic probability rule
(f) Bimodal interpolation rule
(g) Fuzzy-graph interpolation rule
(a) The Robert example
(b) The tall Swedes problem
Deduction (extension) principle
Concluding remark
Acknowledgement
References
Further reading
A Perception-based Web Search withFuzzy Semantic
Abstract
Keywords
Introduction
Internet search with fuzzy semantic deduction
Fuzzy semantic search architecture
Search improvement with fuzzy linguistic semantic
Fuzzy linguistic semantic search steps
Case study
Domains with matching semantic theme
World-wide-web search
Search improvement with fuzzy numeric semantic
Fuzzy Numeric Semantic search steps
Domains with matching semantic themes
World-wide-web search
Conclusion
References
Using Knowledge Trees for Semantic Web Querying
Abstract
Keywords
Introduction
Basic concepts of approximate reasoning
Protoforms
PF-1. Projection protoform
PF-2. Conjunction/projection protoform
PF-3. Modus ponens
PF-4. Weighted modus ponens
PF-5. Modus tollens
PF-6. Extension principle
PF-6b. Inverse extension
PF-7. Fuzzy systems modeling
Knowledge trees
Knowledge trees for directing knowledge search
Non-monotonic possibilistic propositions
Non-monotonic propositions in knowledge trees
Defuzzification
Conclusion
References
XML based Approaches, Building Ontologies, and (Conceptual) Graphs
Fuzzy Data Mining for the Semantic Web:Building XML Mediator Schemas
Abstract
Keywords
Introduction
Schema mining principles
A brief overview of the association rules problem
Schema mining: problem statement
Why considering fuzzy approaches
Fuzzy tree inclusion
Fuzzy vertical paths
Fuzzy horizontal paths
Partial inclusion
Similar trees
Fuzzy frequent subtrees
Conclusion
References
Bottom-up Extraction and Maintenance of Ontology-based Metadata
Abstract
Keywords
Introduction
Communities of practice (CoP) and the semantic web
Building ontologies for CoPs
A bottom-up fuzzy ontology construction and maintenance engine
Encoding XML data
Building document classes using structural information
Content-based document classification
The aggregation problem
An example of class subdivision and refinement
Ad-hoc ontology construction
An example of ad-hoc ontology construction
Class constraints definition
Conclusion
Acknowledgements
References
Approximate Knowledge Graph Retrieval: Measures and Realization
Abstract
Keywords
Introduction
Matching measures for entity types
Similarity between words
Similarity and subsumption between entity types
Matching measures for attribute values
Voting model of fuzzy sets
Conditional probability on fuzzy sets
Similarity and subsumption between fuzzy sets
Storing and querying knowledge graphs
Sesame
SeRQL
Conceptual graphs
Knowledge graph retrieval
Query modification
CG-SeRQL mapping
Approximate knowledge graph matching
VN-KIM query module
Ontology and Knowledge Base
Query editor
Conclusion
References
Integration of Processing of Fuzzy Information
Soft Integration of Information with Semantic Gaps
Abstract
Keywords
Introduction
Background
SOFT – a Structured Object Fusion Toolkit
Overview
How do we derive h?
Representation of approximate correspondences
Updating h with approximate correspondences
Choice of pairs Ri, Sj
Efficiency considerations
When to stop
Implementation and testing
Initial results
Further development
Acknowledgements
References
Processing Fuzzy Information in Semantic Web Applications
Abstract
Keywords
Motivation
World Wide Web
Semantic web
Semantic web applications
Fuzzy control
Fuzzy control in a semantic web
Given ontology and crisp search values:
Given ontology with fuzzy search terms:
Crisp search values without given ontology:
Fuzzy values without given ontology:
Technical, architectural solution/approach
Conclusion and future work
References
Fuzzy Logic Aggregation for Semantic Web Search for the Best (Top-k) Answer
Abstract
Keywords
Introduction
Global score combining particular attribute score.
Cost of web services/disc access.
Declarative semantics of best answer.
Rules become important.
Rules enhancing querying capabilities.
This paper.
Motivating example, solution design, language
Querying the web – web data standards and their translation
Query.
Query continued.
Fuzzy predicate definite logic programming with fuzzy aggregation FDLPa
Language
Truth functions
Structures
Fuzzy definite logic programs and declarative semantics
Procedural semantics of FDLPa
Pavelka completeness of fuzzy definite logic programs by fixpoint semantics
A variant of Generalized Annotated Programs GAP+v
Best/top-k answer semantics
Procedural semantics of best answer
Some additional aspects of our fuzzy rule system
Learning the aggregation function from user evaluation
Discretization – finite domains
Similarities
Tuning similarity transitivity.
Extension of web rule languages
Conclusion
References
Ontologies, Information Retrieval
A Fuzzy Logic Approach to Information Retrieval Using an Ontology-based Representation of Documents
Abstract
Introduction
Concept-based information retrieval
Concept-based representation of documents and queries
Query evaluation
Comparison based on the minimal common sub-tree
Choice of an implication connective
Completing the description of a document/enlarging the query
An illustrative example
Experiments
Document collection
Ontology
Evaluation methodology
Results and discussion
Concluding remarks
Pruning abstract nodes
References
Towards a Semantic Portal for Oncology Using a Description Logic with Fuzzy Concrete Domains
Abstract
Keywords
Introduction
Decision protocol representation in Kasimir
Fuzzy-Kasimir: formalism and inferences
Towards a semantic portal for oncology based on fuzzy datatypes
Protocol representation in the current semantic portal
The future semantic portal, with fuzzy datatypes
Fuzzy datatypes
Subsumption between concepts with fuzzy datatypes ()
alpha-subsumption between concepts with fuzzy datatypes (alpha)
Degree of subsumption between concepts with fuzzy datatypes (F)
Discussion and related work
Fuzzy DLs: how fuzziness is introduced into DLs
Introduction of fuzziness into OWL DL
Conclusion
References
Fuzzy Relational Ontological Model in Information Search Systems
Abstract
Keywords
Introduction
Knowledge based fuzzy information search models
Relational ontological model
Fuzzy relational ontology
Characteristics of the ontological model
Architecture.
Query representation.
Documents representation.
Information retrieval with relational ontological model
Document ranking.
Information search algorithms
Experiments
Evaluation procedure
Experimental results
Conclusions
Acknowledgements
References
Soft Computing in Various Domains
Evolving Ontologies for Intelligent Decision Support
Abstract
Keywords
Introduction
Evolving ontologies and semantic web languages
Ontology and knowledge discovery
Ontology driven knowledge discovery – ODKD
Life cycle of ODKD
ODKD process
Ontology preparation
Ontology population
Instance selection
Ontology mining
Ontology refining
Biomedical evolving ontology case studies
Evolving infogene map
Infogene map ontologies
Concept metadata.
Biomedical domain.
Biomedical informatics domain.
Clinical domain.
Gene ontology.
Disease gene map.
Case studies
The leukemia gene regulatory network map.
Brain disease.
Ontology driven knowledge discovery applications
ODKD and Neucom
Evolving clustering method for hierarchal ontology learning
Ontology on-line analytical pre-processing (OOLAP)
OOLAP alternative pipeline
Ontology visualization.
Data understanding.
Conceptual model mapping.
OLAP tool.
OOLAP biomedical informatics application
Conclusions and future work
References
Enhancing the Power of the Internet Using Fuzzy Logic-based Web Intelligence:Beyond the Semantic Web
Abstract
Keywords
Introduction
Intelligent search for mining of data and textual information
Fuzzy conceptual match and search engine
From search engine to Q/A systems: the need for new tools
NeuSearch™
Intelligent information systems in enterprise
BISC decision support system
Model framework
Fuzzy engine
Application template
User interface
Database (DB)
Measure of association and fuzzy similarity
Evolutionary computing

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